使用熊猫计算不规则时间序列的每日平均值

Question

I am trying to obtain daily averages from an irregular time series from a csv-file.

The data in the csv-file start at 13:00 on 20 September 2013 and run till 10:57 on 14 January 2014:

Time                    Values
20/09/2013 13:00        5.133540
20/09/2013 13:01        5.144993
20/09/2013 13:02        5.158208
20/09/2013 13:03        5.170542
20/09/2013 13:04        5.167899    
20/09/2013 13:25        5.168780
20/09/2013 13:26        5.179351
...

I import them with:

import pandas as pd
data = pd.read_csv('<file name>', parse_dates={'Timestamp':'Time']},index_col='Timestamp')

This results in

                           Values
Timestamp                          
2013-09-20 13:00:00        5.133540
2013-09-20 13:01:00        5.144993
2013-09-20 13:02:00        5.158208
2013-09-20 13:03:00        5.170542
2013-09-20 13:04:00        5.167899
2013-09-20 13:25:00        5.168780
2013-09-20 13:26:00        5.179351
...

And then I do

dataDailyAv = data.resample('D', how = 'mean')

This results in

                  Values
Timestamp                 
2013-01-10        8.623744
2013-01-11             NaN
2013-01-12             NaN
2013-01-13             NaN
2013-01-14             NaN
...

In other words, the result contains dates that do not appear in the original data, and for some of these dates (eg 10 January 2013), there even appears a value.

Any ideas about what is going wrong?

Thanks.

Edit: apparently something goes wrong with the parsing of the date: 01/10/2013 is interpreted as 10 January 2013 instead of 1 October 2013. This can be solved by editing the date format in the csv-file, but is there a way to specify the date format in read_csv?

Answer 1

您需要dayfirst=True ，这是read_csv docs 中列出的众多调整之一。